Automatic Requirements Classification Based on Graph Attention Network

Gang Li

doi:10.1109/ACCESS.2022.3159238

2022-01-01

DOI：10.1109/ACCESS.2022.3159238

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摘要

En 中文

Requirements classification is a significant task for requirements engineering, which is time-consuming and challenging. The traditional requirements classification models usually rely on manual pre-processing and have poor generalization capability. Moreover, these traditional models ignore the sentence structure and syntactic information in requirements. To address these problems, we propose an automatic requirements classification based BERT and graph attention network (GAT), called DBGAT. We construct dependency parse trees and then utilize the GAT for mining the implicit structure feature and syntactic feature of requirements. In addition, we introduce BERT to improve the generalization ability of the model. Experimental results of the PROMISE datasets demonstrate that our proposed DBGAT significantly outperforms existing state-of-the-art methods. Moreover, we investigate the impact of graph construction methods on non-functional requirements classification. DBGAT achieved the best classification results on both seen (F1-scores of up to 91%) and unseen projects (F1-scores of up to 88%), further demonstrating the strong generalization ability.

Keyword:

Syntactics

Bit error rate

Task analysis

Semantics

Natural languages

Requirements engineering

Deep learning

Requirements classification

non-functional requirements

GAT

BERT